Visual Prompt Engineering: Adapting General Models for Various Tasks

Visual Prompt Engineering: Adapting General Models for Various Tasks

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP graduate students, university teachers, and researchers in enterprises. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from … Read more

Important Things Should Be Said Twice! Prompt ‘Repeater’ Significantly Enhances LLM Reasoning Ability

Important Things Should Be Said Twice! Prompt 'Repeater' Significantly Enhances LLM Reasoning Ability

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and researchers in enterprises. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the … Read more

From CLIP to CoOp: A New Paradigm for Visual-Language Models

From CLIP to CoOp: A New Paradigm for Visual-Language Models

Follow the public account “ML_NLP“ Set as “Starred” to receive valuable content promptly! Reprinted from | Smarter Recently, a new paradigm of Prompt has been proposed in the NLP field, aiming to revolutionize the original Fine-tuning method. In the CV field, Prompt can actually be understood as the design of image labels. From this perspective, … Read more

Visual Prompt Engineering: No Fine-Tuning Required

Visual Prompt Engineering: No Fine-Tuning Required

↑ ClickBlue Text Follow the Jishi platform Author丨Tech Beast Editor丨Jishi Platform Jishi Guide How to adapt a pre-trained visual model to new downstream tasks without specific task fine-tuning or any model modifications? >> Join the Jishi CV technology exchange group and stay at the forefront of computer vision Table of Contents 1 Completing Visual Prompting … Read more